# Source code for pysptools.util.data_format

#
#------------------------------------------------------------------------------
# Copyright (c) 2013-2014, Christian Therien
#
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#
# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
#------------------------------------------------------------------------------
#
# data_format.py - This file is part of the PySptools package.
#

"""
convert2d, convert3d, normalize functions
"""

from __future__ import division

import numpy as np

[docs]def convert2d(M):
"""
Converts a 3D data cube (m x n x p) to a 2D matrix of points
where N = m*n.

Parameters:
M: numpy array
A HSI cube (m x n x p).

Returns: numpy array
2D data matrix (N x p)
"""

if M.ndim != 3:
raise RuntimeError('in formating.convert2d,  M have {0} dimension(s), expected 3 dimensions'.format(M.ndim))

h, w, numBands = M.shape

return np.reshape(M, (w*h, numBands))

[docs]def convert3d(M, h, w, sigLast=True):
"""
Converts a 1D (N) or 2D matrix (p x N) or (N x p) to a 3D
data cube (m x n x p) where N = m * n

Parameters:
N: numpy array
1D (N) or 2D data matrix (p x N) or (N x p)

h: integer
Height axis length (or y axis) of the cube.

w: integer
Width axis length (or x axis) of the cube.

siglast: True [default False]
Determine if input N is (p x N) or (N x p).

Returns: numpy array
A 3D data cube (m x n x p)
"""

if M.ndim > 2:
raise RuntimeError('in formating.convert2d,  M have {0} dimension(s), expected 1 or 2 dimensions'.format(M.ndim))

N = np.array(M)

if sigLast == False:
if N.ndim == 1:
return np.reshape(N, (h, w), order='F')
else:
numBands, n = N.shape
return np.reshape(N.transpose(), (h, w, numBands), order='F')

if sigLast == True:
if N.ndim == 1:
return np.reshape(N, (h, w))
else:
numBands, n = N.shape
return np.reshape(N.transpose(), (h, w, numBands), order='F')

[docs]def normalize(M):
"""
Normalizes M to be in range [0, 1].

Parameters:
M: numpy array
1D, 2D or 3D data.

Returns: numpy array
Normalized data.
"""

minVal = np.min(M)
maxVal = np.max(M)

Mn = M - minVal;

if maxVal == minVal:
return np.zeros(M.shape);
else:
return Mn / (maxVal-minVal)